A fundamental challenge of the current electricity infrastructure is incorporating renewable energy sources into the existing grid to make it cheaper, cleaner, more efficient and more reliable. A number of factors make this problem particularly vexing. First, the grid must be have enough generation capacity to accommodate peak demand—days (and hours) when aggregate electricity demand is highest—even though there may be only ten “peak demand” days each year. This means that “pure peaker” power plants (usually fueled by natural gas or diesel) that come on line only to satisfy peak demand remain idle the rest of the year; renewable energy sources like solar and wind power, which are intermittent and unreliable, cannot be used as a substitute.
Second, the daily output of solar and wind can be predicted but it cannot be guaranteed, which makes them poor candidates to supply baseload or load following electricity generation the way conventional fuels (coal, nuclear and natural gas) do.
Third, wind and solar have low inertia; power generated by these sources can be online (or offline) almost instantly. Conversely, a coal or nuclear power plant may require several days to completely cycle up or down, and a load-following natural gas power plant may require 30 to 90 minutes. Thus, one cannot shut off a conventional power plant just because the wind is blowing, or conversely, bring it back online immediately just because the wind has calmed.
Fourth, solar and wind, even though they are highly complementary, do not track well with daily and seasonal demand. On a daily basis, solar generally tracks well with peak demand and thus rates, although solar production tends to decline when demand and prices are highest (e.g., 4 p.m.-6 p.m.). On a seasonal basis, solar energy presents a conundrum in terms of the optimal amount of capacity to install. Electricity use is generally highest in summer (when daily solar radiation begins to decline from its June peak), followed by winter (when solar radiation is lowest), and demand is lowest during the temperate seasons of spring and fall. Thus, installing enough solar capacity to meet electricity demand during summer and winter will result in significant excess capacity in spring and fall. Conversely, installing adequate solar capacity to meet spring and fall demand will result in supply shortfalls during summer and winter months when demand and prices are generally highest.
The value of excess capacity during low demand seasons (spring and fall) is generally low because baseload generation combined with some load-following generation—the two lowest cost tiers of conventional electricity generation—is usually adequate to supply demand. Wind has the opposite problem. It tends to be more available during off-peak times and seasons (e.g., night and winter), and less available during peak times and seasons (daylight hours and summer). When combined, solar and wind can generally provide coverage for all seasons and times of day, but their lack of reliability compared with conventional fuel sources makes this problematic without the ability to store excess capacity from these sources on a large scale.
Fifth, the electric grid has daily needs for ancillary services—typically, multiple intervals on given days when power must be added to or taken off the grid in a matter of seconds to regulate voltage or ensure adequate spinning reserves. Renewable energy sources, given their intermittency, cannot provide power or reduce load when called upon and in fact may exacerbate regulation and reserve challenges because of their inherent volatility and low inertia.
For these reasons, the inclusion of wind and solar into the current grid creates significant volatility issues for price, reserve power and voltage regulation—a problem that will become worse are more renewable energy sources are brought online. Because these renewable energy sources are both intermittent and low inertia, they can create significant disruption to the methodical generation hierarchy of baseload, load following and pure peaker power plants, resulting in significant price volatility. A good example is Texas, which has significant installed wind power capacity and limited capability to acquire or dispatch electricity out of state. Texas also has an aggressive renewable portfolio standard (RPS), which mandated that utilities generate 2,000 megawatts (equivalent to a large nuclear power plant) of new renewable power (primarily wind power) by 2009. The result has been significant volatility in wholesale electricity market prices. In 2009, Texas' average daily spread between minimum and maximum hourly market prices was 22 cents per kilowatt-hour for the Houston market. In 2008 this spread was 41 cents per kWh. (The annual decline can be attributed to significantly reduced electricity demand as a result of the “Great Recession.”) During certain hours, the market price for electricity is negative 150 cents per kWh; these situations occur when significant wind power is online and baseload and load following plants are offline for maintenance or cannot cycle down quickly enough to accommodate the excess capacity.
Similarly, electricity providers must keep electricity in reserve to provide for supply disruptions or demand spikes, and they must regulate voltage and keep it steady. Both imperatives are difficult when renewable sources of power come on and off line abruptly. If the current transmission state includes a significant amount of wind-generated electricity, and the wind stops blowing suddenly, some power reserve must be capable of filling the void immediately to provide demanded power and prevent significant drops in voltage that could result in brown outs. Conversely, if a significant supply of wind or solar comes online suddenly, then a rapid means of absorbing this electricity must be available to avoid over-voltage situations.
These volatility issues pertaining to electricity supply and demand, as well as ancillary services, create significant opportunities for energy optimization. Optimization of electricity supply and demand is not a new concept. Generally, however, optimization has focused on demand side management (DSM), also known as demand response (DR), in which incorporation of renewable energy has been an afterthought. This is true even when DSM-based optimization includes an energy storage component. DSM focuses on reducing demand during peak times—usually, the same times that “pure peaker” plants must be brought online—and has two components: peak shaving and load shifting. Peak shaving focuses on reducing consumption; a typical example is raising one's thermostat a few degrees on a hot summer day.
Load shifting focuses on deferring electric consumption that is necessary but for which timing is discretionary; typical examples include electric clothes drying or running the dishwasher. DSM can result from voluntary measures, automated processes or a combination. Automated processes typically include individual control of appliances via a home automated network (HAN), from which residential consumption can be shifted automatically to off-peak hours. The inclusion of some type of energy storage, such as a battery, can be integrated into a DSM scheme in a way that provides opportunity for both arbitrage based on differences in peak and off-peak rates and reductions to peak demand charges. In typical optimization schemes, the battery stores excess capacity from renewable sources like solar and wind but this is not typically integrated into an overall optimization calculus based on predictive factors.
Optimization of electricity supply and demand for a building or network of buildings, inclusive of renewable energy sources and storage, can be improved significantly beyond prior approaches by integrating three predictable factors: (1) the wholesale market price of electricity; (2) the availability of renewable energy; and (3) the building's consumption. Each of these factors can be predicted with significant accuracy for the next 24 hours. Regional transmission organizations (RTOs) publish predicted wholesale hourly prices, also known as “day ahead” rates, in advance of the market day.
Weather predictions can provide data related to ambient hourly temperature, which is a significant factor in a building's consumption, as well as the hourly availability of solar radiation or wind. A building's hourly electric consumption can be predicted with significant accuracy based on historical factors, weather (e.g., combined heat and humidity), and both demographic and physical considerations that form a “load profile” (e.g., square footage, weekday/weekend/holiday, home office use). With these factors included in the optimization calculus, a building that incorporates renewable energy, grid power and storage can engage in electricity arbitrage and peak power reductions. Power can be acquired and stored when it is cheap, free or even at time when the utility will pay the building owner to take power off the grid. The stored power can then be used later when the real-time price of electricity is high.
This network of distributed generation and storage can be scaled into a virtual power plant to provide even greater economic benefits, as the resulting network becomes an immediate source of reliable power for ancillary services and peak power. Generally, ancillary services provide greater economic benefit than arbitrage, with frequency regulation providing the highest value. The owner of electricity storage must be available to reduce load (or in same cases, increase it) in a matter of seconds and must have a reliable supply (or sink) of power to do so. The invention can determine the allocation of networked storage and control it at the individual building level, based upon the need and value of services storage can provide (frequency regulation, spinning reserve, peak power and arbitrage) and thus maximize economic value for both individual building owners, groupings of building owners (e.g., a commercial campus or tract-home development) and the network as a whole.
An additional benefit of the invention is that it adheres to the “one way” structure of the electricity delivery system. Generally, the grid has been designed for a process in which electricity is generated by large power plants (whether nuclear, fossil fuel-based or wind), and then transmitted over large “arteries,” from which point it is distributed at multiple points and consumed locally. The system was not well-designed to accommodate the reverse—power generated at thousands of points of consumption and placed back on the transmission grid—even though this is what “net metering” schemes require and “smart grid” promises. A simpler, better way to accomplish utilities' objectives of access to immediate, reliable power than placing it back on the grid is to avoid pulling it off. The present invention teaches such a method, which has the added advantage of keeping all activity on the customer side of meter. This attribute protects the building owner from being subject to regulatory approval.